The ILWAS Model: Formulation and Application

Gherini, Steven A.; Mok, Lingfung; Hudson, Robert J.; Davis, George F.; Chen, Carl W.; Goldstein, Robert A.

doi:10.1007/978-94-009-5498-4_7

Cited by 62 publications

(87 citation statements)

References 5 publications

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“…Later models followed this approach, expanded the number of ionic species modeled, and included nutrient concentrations. Papers describing physical models now constitute 50% of the literature, with MAGIC (Cosby et al 1985), ILWAS (Goldstein et al 1984, Gherini et al 1985, PnET (Aber and Federer 1992), and INCA (Whitehead et al 1998) models among those most often used. These models can make temporal predictions, but they cannot predict water chemistry at unmeasured locations or be used to assess the impact of land use because they must be calibrated with water chemistry measured at each site.…”

Section: Predicting Water-chemistry Reference Conditionsmentioning

confidence: 99%

Theoretical predictions of observed to expected ratios in RIVPACS-type predictive model assessments of stream biological condition

Yuan

2006

Journal of the North American Benthological Society

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Section: Predicting Water-chemistry Reference Conditionsmentioning

confidence: 99%

Theoretical predictions of observed to expected ratios in RIVPACS-type predictive model assessments of stream biological condition

Yuan

2006

Journal of the North American Benthological Society

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“…Gherini et al (1985) developed an integrated lake-watershed acidification study (IL-WAS) model. Graham et al (1991) demonstrated a spatially explicit model of the interactive effects of ozone and bark beetles on forest pattern and water quality.…”

Section: Simulation Modelsmentioning

confidence: 99%

Developing a Field of Landscape Ecotoxicology

Cairns¹,

Niederlehner²

1996

Ecological Applications

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JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact support@jstor.org.. Abstract. Since toxicants are spread over ecological landscapes, it seems likely that they have effects at that level of ecological organization. Landscape ecotoxicology examines the effects of toxic chemicals on larger scales than traditional environmental toxicology. This approach is characterized by the use of endpoints appropriate to the spatial scale across which a toxicant is dispersed, attention to interactions between physical and temporal patterns and the process of ecological impairment, and integration of multiple lines of evidence for toxicity at various scales. In addition, landscape ecotoxicology seeks predictive models in order to influence human actions before environmental damage occurs. Integrating information from damaged systems, toxicity tests, simulation models, and biomonitoring of healthy systems provides the best basis for decisions. Rapid progress in landscape ecotoxicology is expected as scientists incorporate tools, such as remote sensing and spatially explicit simulation models, and then calibrate these models using data from long.-term biomonitoring of large areas. Further integration into combined socio-economic-ecological models is also possible. Ecological Society of America

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“…In spite of this complexity, numerous successful hydro-chemical models have been developed over recent decades for predicting, for individual streams, the temporal changes in water chemical quality during and following precipitation events (e.g. Christopherson et al, 1982;Gherini et al, 1985;Nikolaidis et al,1988;Christopherson et al, 1994). Process-based models may be applied over longer time scales too (Cosby et al, 1985;Whitehead et al, 1988), although care is then needed to ensure that the model encompasses all key parameters driving water chemistry if extrapolation is made significantly beyond the calibration period.…”

Section: Introductionmentioning

confidence: 99%

Can sediment data be used to predict alkalinity and base cation chemistry of surface waters?

Begum

McClean

Cresser

et al. 2010

Science of The Total Environment

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We hypothesise that stream sediment elemental composition can predict mean and minimum concentrations of alkalinity, Ca and Mg in the river water throughout a river network. We tested this hypothesis for the River Derwent catchment in North Yorkshire, England, by using 6 years of water chemistry data from the Environment Agency and a digital elevation model to flow pathweight British Geological Survey (BGS) sediment element concentration data.The predictive models for mean concentrations were excellent for Ca and alkalinity, but less good for Mg, and did not require land use data inputs as stream water sediment composition seems to reflect all aspects of the riparian zone soil system. Predictive model forms were linear. Attempts to predict minimum values for Ca and alkalinity also were less satisfactory. This probably is due to variations in hydrological response times to individual precipitation events across the catchment.

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The ILWAS Model: Formulation and Application

Cited by 62 publications

References 5 publications

Theoretical predictions of observed to expected ratios in RIVPACS-type predictive model assessments of stream biological condition

Theoretical predictions of observed to expected ratios in RIVPACS-type predictive model assessments of stream biological condition

Developing a Field of Landscape Ecotoxicology

Can sediment data be used to predict alkalinity and base cation chemistry of surface waters?

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